Package weka.classifiers.meta
Class RandomSubSpace
- java.lang.Object
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- All Implemented Interfaces:
java.io.Serializable
,java.lang.Cloneable
,CapabilitiesHandler
,OptionHandler
,Randomizable
,RevisionHandler
,TechnicalInformationHandler
,WeightedInstancesHandler
public class RandomSubSpace extends RandomizableIteratedSingleClassifierEnhancer implements WeightedInstancesHandler, TechnicalInformationHandler
This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity. The classifier consists of multiple trees constructed systematically by pseudorandomly selecting subsets of components of the feature vector, that is, trees constructed in randomly chosen subspaces.
For more information, see
Tin Kam Ho (1998). The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(8):832-844. URL http://citeseer.ist.psu.edu/ho98random.html. BibTeX:@article{Ho1998, author = {Tin Kam Ho}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, number = {8}, pages = {832-844}, title = {The Random Subspace Method for Constructing Decision Forests}, volume = {20}, year = {1998}, ISSN = {0162-8828}, URL = {http://citeseer.ist.psu.edu/ho98random.html} }
Valid options are:-P Size of each subspace: < 1: percentage of the number of attributes >=1: absolute number of attributes
-S <num> Random number seed. (default 1)
-I <num> Number of iterations. (default 10)
-D If set, classifier is run in debug mode and may output additional info to the console
-W Full name of base classifier. (default: weka.classifiers.trees.REPTree)
Options specific to classifier weka.classifiers.trees.REPTree:
-M <minimum number of instances> Set minimum number of instances per leaf (default 2).
-V <minimum variance for split> Set minimum numeric class variance proportion of train variance for split (default 1e-3).
-N <number of folds> Number of folds for reduced error pruning (default 3).
-S <seed> Seed for random data shuffling (default 1).
-P No pruning.
-L Maximum tree depth (default -1, no maximum)
Options after -- are passed to the designated classifier.- Version:
- $Revision: 1.4 $
- Author:
- Bernhard Pfahringer (bernhard@cs.waikato.ac.nz), Peter Reutemann (fracpete@cs.waikato.ac.nz)
- See Also:
- Serialized Form
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Constructor Summary
Constructors Constructor Description RandomSubSpace()
Constructor.
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description void
buildClassifier(Instances data)
builds the classifier.double[]
distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance.java.lang.String[]
getOptions()
Gets the current settings of the Classifier.java.lang.String
getRevision()
Returns the revision string.double
getSubSpaceSize()
Gets the size of each subSpace, as a percentage of the training set size.TechnicalInformation
getTechnicalInformation()
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.java.lang.String
globalInfo()
Returns a string describing classifierjava.util.Enumeration
listOptions()
Returns an enumeration describing the available options.static void
main(java.lang.String[] args)
Main method for testing this class.void
setOptions(java.lang.String[] options)
Parses a given list of options.void
setSubSpaceSize(double value)
Sets the size of each subSpace, as a percentage of the training set size.java.lang.String
subSpaceSizeTipText()
Returns the tip text for this propertyjava.lang.String
toString()
Returns description of the bagged classifier.-
Methods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
getSeed, seedTipText, setSeed
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Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer
getNumIterations, numIterationsTipText, setNumIterations
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Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, getCapabilities, getClassifier, setClassifier
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Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
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Method Detail
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globalInfo
public java.lang.String globalInfo()
Returns a string describing classifier- Returns:
- a description suitable for displaying in the explorer/experimenter gui
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getTechnicalInformation
public TechnicalInformation getTechnicalInformation()
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.- Specified by:
getTechnicalInformation
in interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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listOptions
public java.util.Enumeration listOptions()
Returns an enumeration describing the available options.- Specified by:
listOptions
in interfaceOptionHandler
- Overrides:
listOptions
in classRandomizableIteratedSingleClassifierEnhancer
- Returns:
- an enumeration of all the available options.
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setOptions
public void setOptions(java.lang.String[] options) throws java.lang.Exception
Parses a given list of options. Valid options are:-P Size of each subspace: < 1: percentage of the number of attributes >=1: absolute number of attributes
-S <num> Random number seed. (default 1)
-I <num> Number of iterations. (default 10)
-D If set, classifier is run in debug mode and may output additional info to the console
-W Full name of base classifier. (default: weka.classifiers.trees.REPTree)
Options specific to classifier weka.classifiers.trees.REPTree:
-M <minimum number of instances> Set minimum number of instances per leaf (default 2).
-V <minimum variance for split> Set minimum numeric class variance proportion of train variance for split (default 1e-3).
-N <number of folds> Number of folds for reduced error pruning (default 3).
-S <seed> Seed for random data shuffling (default 1).
-P No pruning.
-L Maximum tree depth (default -1, no maximum)
Options after -- are passed to the designated classifier.- Specified by:
setOptions
in interfaceOptionHandler
- Overrides:
setOptions
in classRandomizableIteratedSingleClassifierEnhancer
- Parameters:
options
- the list of options as an array of strings- Throws:
java.lang.Exception
- if an option is not supported
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getOptions
public java.lang.String[] getOptions()
Gets the current settings of the Classifier.- Specified by:
getOptions
in interfaceOptionHandler
- Overrides:
getOptions
in classRandomizableIteratedSingleClassifierEnhancer
- Returns:
- an array of strings suitable for passing to setOptions
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subSpaceSizeTipText
public java.lang.String subSpaceSizeTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getSubSpaceSize
public double getSubSpaceSize()
Gets the size of each subSpace, as a percentage of the training set size.- Returns:
- the subSpace size, as a percentage.
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setSubSpaceSize
public void setSubSpaceSize(double value)
Sets the size of each subSpace, as a percentage of the training set size.- Parameters:
value
- the subSpace size, as a percentage.
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buildClassifier
public void buildClassifier(Instances data) throws java.lang.Exception
builds the classifier.- Overrides:
buildClassifier
in classIteratedSingleClassifierEnhancer
- Parameters:
data
- the training data to be used for generating the classifier.- Throws:
java.lang.Exception
- if the classifier could not be built successfully
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distributionForInstance
public double[] distributionForInstance(Instance instance) throws java.lang.Exception
Calculates the class membership probabilities for the given test instance.- Overrides:
distributionForInstance
in classClassifier
- Parameters:
instance
- the instance to be classified- Returns:
- preedicted class probability distribution
- Throws:
java.lang.Exception
- if distribution can't be computed successfully
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toString
public java.lang.String toString()
Returns description of the bagged classifier.- Overrides:
toString
in classjava.lang.Object
- Returns:
- description of the bagged classifier as a string
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getRevision
public java.lang.String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
public static void main(java.lang.String[] args)
Main method for testing this class.- Parameters:
args
- the options
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